Smart Hybrid Maintenance as a Pathway to Energy-Efficient Manufacturing

The growing demand for energy-efficient and sustainable manufacturing requires maintenance strategies that extend beyond reliability optimization toward active energy management. This study proposes a Smart Hybrid Maintenance System (SHMS) that integrates Reliability-Centered Maintenance (RCM) and Condition-Based Maintenance (CBM) principles with energy performance assessment. The framework combines classical reliability indicators (MTBF, MTTR, and Availability) with energy-oriented Key Performance Indicators (EEI, EENS, and OEE) to quantify the relationship between machine degradation, operational availability, and energy efficiency. The methodology was validated using two datasets: NASA N-CMAPSS for simulation-based benchmarking and the Smart RDM industrial environment for real process data. Results demonstrate that predictive maintenance supported by the Hybrid Risk Index (HRI) reduces unplanned downtime by up to 12%, corresponding to a 7–9% decrease in specific energy consumption and a measurable improvement in the Energy Efficiency Index. By embedding energy metrics into predictive maintenance decision-making, the SHMS enables dual optimization of reliability and energy performance. The proposed approach not only enhances equipment availability and cost efficiency but also supports industrial decarbonization targets, positioning predictive maintenance as a key enabler of energy-aware and sustainable manufacturing aligned with Industry 5.0 objectives.